--- title: fastai U-Net keywords: fastai sidebar: home_sidebar ---
%reload_ext autoreload
%autoreload 2
%matplotlib inline
import sys
sys.path.append('..')
from superres.datasets import *
from superres.databunch import *
seed = 8610
random.seed(seed)
np.random.seed(seed)
train_hr = div2k_train_hr_crop_256
in_size = 256
out_size = 256
scale = 4
bs = 10
data = create_sr_databunch(train_hr, in_size=in_size, out_size=out_size, scale=scale, bs=bs, seed=seed)
print(data)
data.show_batch()
ImageDataBunch; Train: LabelList (25245 items) x: ImageImageList Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256) y: ImageImageList Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256) Path: /home/jovyan/notebook/datasets/DIV2K/DIV2K_train_HR_crop/256; Valid: LabelList (6311 items) x: ImageImageList Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256) y: ImageImageList Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256),Image (3, 256, 256) Path: /home/jovyan/notebook/datasets/DIV2K/DIV2K_train_HR_crop/256; Test: None
model = models.resnet34
loss_func = MSELossFlat()
metrics = [m_psnr, m_ssim]
wd = 1e-3
y_range = (-3.,3.)
model_name = 'sr_unet'
learn = unet_learner(data, model, wd=wd, metrics=metrics, y_range=y_range, loss_func=loss_func,
blur=True, norm_type=NormType.Weight, self_attention=True)
learn.path = Path('.')
lr_find(learn)
learn.recorder.plot(suggestion=True)
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
Min numerical gradient: 1.91E-04
Min loss divided by 10: 1.10E-01
lr = 1e-3
lrs = slice(lr)
epoch = 3
pct_start = 0.3
wd = 1e-3
save_fname = model_name
callbacks = [ShowGraph(learn), SaveModelCallback(learn, name=save_fname)]
learn.fit_one_cycle(epoch, lrs, pct_start=pct_start, wd=wd, callbacks=callbacks)
| epoch | train_loss | valid_loss | m_psnr | m_ssim | time |
|---|---|---|---|---|---|
| 0 | 0.090948 | 0.057407 | 32.308601 | 0.425962 | 09:00 |
| 1 | 0.079309 | 0.054548 | 32.957603 | 0.435841 | 08:58 |
| 2 | 0.073125 | 0.053997 | 33.810516 | 0.441839 | 08:59 |
Better model found at epoch 0 with valid_loss value: 0.05740687996149063. Better model found at epoch 1 with valid_loss value: 0.054548367857933044. Better model found at epoch 2 with valid_loss value: 0.05399680882692337.
learn.show_results()
test_hr = set14_hr
il_test_x = ImageImageList.from_folder(test_hr, after_open=partial(after_open_image, size=in_size, scale=4, sizeup=True))
il_test_y = ImageImageList.from_folder(test_hr, after_open=partial(after_open_image, size=out_size))
_ = learn.load(save_fname)
sr_test(learn, il_test_x, il_test_y, model_name)
bicubic: PSNR:24.11,SSIM:0.7822 sr_unet: PSNR:24.98,SSIM:0.8146
model
<function torchvision.models.resnet.resnet34(pretrained=False, progress=True, **kwargs)>
learn.summary()
DynamicUnet ====================================================================== Layer (type) Output Shape Param # Trainable ====================================================================== Conv2d [64, 128, 128] 9,408 False ______________________________________________________________________ BatchNorm2d [64, 128, 128] 128 True ______________________________________________________________________ ReLU [64, 128, 128] 0 False ______________________________________________________________________ MaxPool2d [64, 64, 64] 0 False ______________________________________________________________________ Conv2d [64, 64, 64] 36,864 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ ReLU [64, 64, 64] 0 False ______________________________________________________________________ Conv2d [64, 64, 64] 36,864 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ Conv2d [64, 64, 64] 36,864 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ ReLU [64, 64, 64] 0 False ______________________________________________________________________ Conv2d [64, 64, 64] 36,864 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ Conv2d [64, 64, 64] 36,864 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ ReLU [64, 64, 64] 0 False ______________________________________________________________________ Conv2d [64, 64, 64] 36,864 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ Conv2d [128, 32, 32] 73,728 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ ReLU [128, 32, 32] 0 False ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ Conv2d [128, 32, 32] 8,192 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ ReLU [128, 32, 32] 0 False ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ ReLU [128, 32, 32] 0 False ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ ReLU [128, 32, 32] 0 False ______________________________________________________________________ Conv2d [128, 32, 32] 147,456 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ Conv2d [256, 16, 16] 294,912 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ ReLU [256, 16, 16] 0 False ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [256, 16, 16] 32,768 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ ReLU [256, 16, 16] 0 False ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ ReLU [256, 16, 16] 0 False ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ ReLU [256, 16, 16] 0 False ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ ReLU [256, 16, 16] 0 False ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ ReLU [256, 16, 16] 0 False ______________________________________________________________________ Conv2d [256, 16, 16] 589,824 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [512, 8, 8] 1,179,648 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ ReLU [512, 8, 8] 0 False ______________________________________________________________________ Conv2d [512, 8, 8] 2,359,296 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ Conv2d [512, 8, 8] 131,072 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ Conv2d [512, 8, 8] 2,359,296 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ ReLU [512, 8, 8] 0 False ______________________________________________________________________ Conv2d [512, 8, 8] 2,359,296 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ Conv2d [512, 8, 8] 2,359,296 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ ReLU [512, 8, 8] 0 False ______________________________________________________________________ Conv2d [512, 8, 8] 2,359,296 False ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ BatchNorm2d [512, 8, 8] 1,024 True ______________________________________________________________________ ReLU [512, 8, 8] 0 False ______________________________________________________________________ Conv2d [1024, 8, 8] 4,719,616 True ______________________________________________________________________ ReLU [1024, 8, 8] 0 False ______________________________________________________________________ Conv2d [512, 8, 8] 4,719,104 True ______________________________________________________________________ ReLU [512, 8, 8] 0 False ______________________________________________________________________ Conv2d [1024, 8, 8] 525,312 True ______________________________________________________________________ PixelShuffle [256, 16, 16] 0 False ______________________________________________________________________ ReplicationPad2d [256, 17, 17] 0 False ______________________________________________________________________ AvgPool2d [256, 16, 16] 0 False ______________________________________________________________________ ReLU [1024, 8, 8] 0 False ______________________________________________________________________ BatchNorm2d [256, 16, 16] 512 True ______________________________________________________________________ Conv2d [512, 16, 16] 2,359,808 True ______________________________________________________________________ ReLU [512, 16, 16] 0 False ______________________________________________________________________ Conv2d [512, 16, 16] 2,359,808 True ______________________________________________________________________ ReLU [512, 16, 16] 0 False ______________________________________________________________________ ReLU [512, 16, 16] 0 False ______________________________________________________________________ Conv2d [1024, 16, 16] 525,312 True ______________________________________________________________________ PixelShuffle [256, 32, 32] 0 False ______________________________________________________________________ ReplicationPad2d [256, 33, 33] 0 False ______________________________________________________________________ AvgPool2d [256, 32, 32] 0 False ______________________________________________________________________ ReLU [1024, 16, 16] 0 False ______________________________________________________________________ BatchNorm2d [128, 32, 32] 256 True ______________________________________________________________________ Conv2d [384, 32, 32] 1,327,488 True ______________________________________________________________________ ReLU [384, 32, 32] 0 False ______________________________________________________________________ Conv2d [384, 32, 32] 1,327,488 True ______________________________________________________________________ ReLU [384, 32, 32] 0 False ______________________________________________________________________ Conv1d [48, 1024] 18,432 True ______________________________________________________________________ Conv1d [48, 1024] 18,432 True ______________________________________________________________________ Conv1d [384, 1024] 147,456 True ______________________________________________________________________ ReLU [384, 32, 32] 0 False ______________________________________________________________________ Conv2d [768, 32, 32] 295,680 True ______________________________________________________________________ PixelShuffle [192, 64, 64] 0 False ______________________________________________________________________ ReplicationPad2d [192, 65, 65] 0 False ______________________________________________________________________ AvgPool2d [192, 64, 64] 0 False ______________________________________________________________________ ReLU [768, 32, 32] 0 False ______________________________________________________________________ BatchNorm2d [64, 64, 64] 128 True ______________________________________________________________________ Conv2d [256, 64, 64] 590,080 True ______________________________________________________________________ ReLU [256, 64, 64] 0 False ______________________________________________________________________ Conv2d [256, 64, 64] 590,080 True ______________________________________________________________________ ReLU [256, 64, 64] 0 False ______________________________________________________________________ ReLU [256, 64, 64] 0 False ______________________________________________________________________ Conv2d [512, 64, 64] 131,584 True ______________________________________________________________________ PixelShuffle [128, 128, 128] 0 False ______________________________________________________________________ ReplicationPad2d [128, 129, 129] 0 False ______________________________________________________________________ AvgPool2d [128, 128, 128] 0 False ______________________________________________________________________ ReLU [512, 64, 64] 0 False ______________________________________________________________________ BatchNorm2d [64, 128, 128] 128 True ______________________________________________________________________ Conv2d [96, 128, 128] 165,984 True ______________________________________________________________________ ReLU [96, 128, 128] 0 False ______________________________________________________________________ Conv2d [96, 128, 128] 83,040 True ______________________________________________________________________ ReLU [96, 128, 128] 0 False ______________________________________________________________________ ReLU [192, 128, 128] 0 False ______________________________________________________________________ Conv2d [384, 128, 128] 37,248 True ______________________________________________________________________ PixelShuffle [96, 256, 256] 0 False ______________________________________________________________________ ReLU [384, 128, 128] 0 False ______________________________________________________________________ MergeLayer [99, 256, 256] 0 False ______________________________________________________________________ Conv2d [99, 256, 256] 88,308 True ______________________________________________________________________ ReLU [99, 256, 256] 0 False ______________________________________________________________________ Conv2d [99, 256, 256] 88,308 True ______________________________________________________________________ ReLU [99, 256, 256] 0 False ______________________________________________________________________ MergeLayer [99, 256, 256] 0 False ______________________________________________________________________ Conv2d [3, 256, 256] 300 True ______________________________________________________________________ SigmoidRange [3, 256, 256] 0 False ______________________________________________________________________ Total params: 41,405,588 Total trainable params: 20,137,940 Total non-trainable params: 21,267,648 Optimized with 'torch.optim.adam.Adam', betas=(0.9, 0.99) Using true weight decay as discussed in https://www.fast.ai/2018/07/02/adam-weight-decay/ Loss function : FlattenedLoss ====================================================================== Callbacks functions applied